code.zip
From the dataset abstract
考量病理影像過於龐大，使得標註成本過高，以及了解到若能聯合多間醫療院所共同訓練 AI 模型，可有效降低標註需求，發展一種基於聯邦式學習並引入多倍率概念的公私模型交互監督之半監督聯邦式學習，藉由少量標註成本學習複雜的病理影像資訊。
Additional Information
| Field | Value |
|---|---|
| Data last updated | September 30, 2025 |
| Metadata last updated | September 30, 2025 |
| Created | September 30, 2025 |
| Format | application/zip |
| License | Other (Non-Commercial) |
| created | 1 month ago |
| format | ZIP |
| id | fd5c28d6-47d8-4dc3-9f1b-cc8c4568f11e |
| last modified | 1 month ago |
| md5 | 4adcbf5ca821d296943d67511a250555 |
| mimetype | application/zip |
| on same domain | True |
| package id | 20dfb4d0-5004-4164-8884-21c288cea207 |
| position | 1 |
| proxy url | https://scidm.nchc.org.tw/en/dataset/20dfb4d0-5004-4164-8884-21c288cea207/resource/fd5c28d6-47d8-4dc3-9f1b-cc8c4568f11e/nchcproxy/code.zip |
| revision id | 682a160f-3ccf-48ef-9701-dea5c1c8df53 |
| sha256 | 6767b05e9ee795260909911a878ea8acb166593f49a4d97c44552aff70dbf286 |
| size | 795.7 KiB |
| state | active |
| url type | upload |
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